8
Do Cross-Country Variations in Social Integration and Social Interactions Explain Differences in Life Expectancy in Industrialized Countries?

James Banks, Lisa Berkman, and James P. Smithwith Mauricio Avendano and Maria Glymour

INTRODUCTION

Variations in life expectancy among industrialized countries have been attributed to differences in patterns of health behavior, health care, socioeconomic conditions, and variations in social and economic policies. In this chapter, we explore whether variations in morbidity, mortality, and life expectancy are related to variations in the extent to which countries have different levels of social integration or social support. Extensive research suggests that aspects of social networks and social integration may be associated with mortality in a number of countries (Berkman and Syme, 1979; Berkman et al., 2004; Blazer, 1982; Fuhrer and Stansfeld, 2002; Fuhrer et al., 1999; House, Robbins, and Metzner, 1982; Kaplan et al., 1988; Khang and Kim, 2005; Orth-Gomer and Johnson, 1987; Orth-Gomer, Rosengren, and Wilhelmsen, 1993; Orth-Gomer, Unden, and Edwards, 1988; Orth-Gomer et al., 1998; Penninx et al., 1998; Sugisawa, Liang, and Liu, 1994; Welin et al., 1985). But in no studies have we been able to compare either risks or distributions of comparably defined social networks across countries, nor have we been able to understand if variations in social networks and social participation might explain cross-country variations in population health.

We explore these issues from several perspectives. Ideally, we want to assess the variability in distributions of social networks and support in many countries. We would also like to identify whether risks associated with social isolation and various health outcomes are the same in each country. For social networks and support to “explain” cross-country differences in life expectancy, at least one of two conditions must be met. First, a differ-

Citation Manager

"
8 Do Cross-Country Variations in Social Integration and Social Interactions Explain Differences in Life Expectancy in Industrialized Countries?--James Banks, Lisa Berkman, and James P. Smith with Mauricio Avendano and Maria Glymour ."
International Differences in Mortality at Older Ages: Dimensions and Sources . Washington, DC: The National Academies Press,
2011 .

Please select a format:

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 217
8
Do Cross-Country Variations
in Social Integration and
Social Interactions Explain
Differences in Life Expectancy
in Industrialized Countries?
James Banks, Lisa Berkman, and James P. Smith
with Mauricio Aendano and Maria Glymour
INTRODUCTION
Variations in life expectancy among industrialized countries have been
attributed to differences in patterns of health behavior, health care, socio-
economic conditions, and variations in social and economic policies. In
this chapter, we explore whether variations in morbidity, mortality, and life
expectancy are related to variations in the extent to which countries have
different levels of social integration or social support. Extensive research
suggests that aspects of social networks and social integration may be asso -
ciated with mortality in a number of countries (Berkman and Syme, 1979;
Berkman et al., 2004; Blazer, 1982; Fuhrer and Stansfeld, 2002; Fuhrer
et al., 1999; House, Robbins, and Metzner, 1982; Kaplan et al., 1988;
Khang and Kim, 2005; Orth-Gomer and Johnson, 1987; Orth-Gomer,
Rosengren, and Wilhelmsen, 1993; Orth-Gomer, Unden, and Edwards,
1988; Orth-Gomer et al., 1998; Penninx et al., 1998; Sugisawa, Liang,
and Liu, 1994; Welin et al., 1985). But in no studies have we been able to
compare either risks or distributions of comparably defined social networks
across countries, nor have we been able to understand if variations in social
networks and social participation might explain cross-country variations
in population health.
We explore these issues from several perspectives. Ideally, we want
to assess the variability in distributions of social networks and support in
many countries. We would also like to identify whether risks associated with
social isolation and various health outcomes are the same in each country.
For social networks and support to “explain” cross-country differences in
life expectancy, at least one of two conditions must be met. First, a differ-

OCR for page 217
INTERNATIONAL DIFFERENCES IN MORTALITY AT OLDER AGES
ent fraction of the population needs to be exposed to risk factors across
countries. Second, the health risk—“toxicity”—associated with risk factors
might differ between countries. For common risk factors, even small dif-
ferences in toxicity may have large population health effects. Differences
in toxicity could occur if population differences in exacerbating or com-
pensatory factors influence the risk of disease. For example, if countries
had public policies protecting citizens against deleterious health effects of
extreme poverty, we might not see health effects manifest themselves there,
even though poverty was present. Third, we would hope to assess in a single
model whether social integration and support can account for cross-country
differences in life expectancy. In this chapter we examine the first two but
do not have adequate data to test the third in a compelling way, except for
a comparison of England and the United States.
The lack of truly harmonized individual-level data across countries
on relevant exposures and health outcomes over time limits our ability to
examine this question. To overcome this limitation, we start by comparing
associations between social integration and social support in the United
States and England, using data from the Health and Retirement Survey
(HRS) and the English Longitudinal Study of Ageing (ELSA). Although not
identical, these surveys have very comparable measurements of social net-
works and social support, as well as comparable data on health conditions
and associated risks. We then consider ways in which related psychosocial
conditions tapping dimensions of stress may explain observed health varia-
tions between the United States and England. We examine these questions
for a variety of self-reported outcomes and measured biomarkers of disease.
In addition, we use the mortality follow-up in HRS and ELSA to examine
impacts of social networks and interactions on all-cause mortality.
Since differences in life expectancy between the United States and Eng-
land are relatively small, we then examine how 28 industrialized countries
vary on several dimensions of social networks and support. In these analy-
ses, we draw on recent data from the Gallup World Poll for Japan and a
number of European and North American countries. We present data on the
distribution of dimensions of social integration explored in our HRS/ELSA
comparisons. Although the items are not fully identical, they provide us
with a general overview of variations in these dimensions in a wider set of
countries. We conclude with suggestions for carrying this work forward by
exploring whether variability in social networks is related to a country’s
level of health and well-being.
The chapter is divided into four sections. First, we compare morbid-
ity and health risks in England and the United States by social networks
and support, using cross-sectional data from HRS and ELSA. Second, we
briefly report on whether other psychosocial stressors often related to social
networks may help explain cross-country differences. Third, we examine

OCR for page 217
VARIATIONS IN SOCIAL INTEGRATION AND SOCIAL INTERACTIONS
mortality risks associated with these social networks in ELSA and HRS. In
the last section, we use data from Gallup to examine the extent to which
countries vary on domains related to social networks, social integration,
and support.
We were unable to explore whether social networks actually explain
diverging trends in life expectancy because we do not have data on long-
term trends in these conditions across countries. However, this is a first
attempt at addressing this question by exploring whether such conditions
are able to explain variations in health outcomes contemporaneously and
whether variations are large enough in and of themselves to be able to
explain diverging trends. We conclude with a summary of our findings and
a discussion of strengths and weaknesses of the work as well as ideas for
how to extend work in this area.
SOCIAL NETWORKS, SUPPORT, AND HEALTH
IN THE UNITED STATES AND ENGLAND
In this section we provide a descriptive portrait of social networks and
social support of older residents in the United States and England and ex-
amine their association with health outcomes. We concentrate on the United
States and England because the most comparable, comprehensive data on
social networks and social support are available for them. A recent study
(Banks et al., 2006) documented large health differences between England
and the United States, and it is possible that social network and social sup-
port differences may explain the U.S. disadvantage in health.
Data
For the United States, our research is based on the Health and Retire-
ment Survey, a nationally representative survey that now includes more
than 20,000 people over age 50 in the United States (Juster and Suzman,
1995). HRS began in 1991, and new cohorts have been subsequently added
to maintain population representation of this age segment. Respondents are
reinterviewed biannually.
For England, we use the English Longitudinal Survey of Ageing, which
contains around 12,000 respondents recruited from 3 separate years of the
Health Survey for England (HSE) providing representative samples of
the English population age 50 and over (Marmot et al., 2002). The health
data were supplemented by social and economic data collected in the first
ELSA wave, fielded in 2002. Like HRS, the initial baseline sample was of
the noninstitutionized population, and follow-ups (including of those sub-
sequently moving into institutions) are conducted every 2 years. However,
since the ELSA study is still a younger study, in the sense that the baseline

OCR for page 217
0 INTERNATIONAL DIFFERENCES IN MORTALITY AT OLDER AGES
is more recent, it will presumably be less representative of the entire popula-
tion age 50 and over (including those in institutions).
For our analysis we selected key health and social network and support
constructs in which strong a priori measurement comparability existed. The
2004 waves of ELSA and HRS were used for analysis, since this was the year
in which HRS first contained social network and social support variables
directly comparable to those collected in ELSA.
Measures of Chronic Conditions, Biomarkers of
Disease Risk, and Health Behaviors
Both surveys collect data on individual self-reports of diseases in the
form “Did a doctor ever tell you that you had ___?” In addition, both stud-
ies have biomarkers of diabetes risk (HbA1c) and have assessed blood pres-
sure. These two biomarkers permit us to assess diabetes and hypertension
status more reliably. The specific diseases analyzed include diabetes (assessed
by either self-report of diabetes or HbA1c over 6.5 percent), hypertension
(assessed by measured systolic blood pressure ≥ 140 or diastolic blood
pressure ≥ 90 or self-report of hypertensive medication), self-reported heart
disease, pulmonary function (using a clinical assessment of peak flow), and
obesity (body mass index, BMI, ≥ 30).
Lung function in HRS was measured using peak flow (averaged over
3 measures), and in ELSA it was measured with forced expiatory volume
(FEV). To account for this difference, we show parameter estimates for each
social indicator as a percentage of the average for the reference group. These
measures operate similarly with this transformation, as the effect estimated
for smoking on lung function is similar in both HRS and ELSA. The two
surveys also collect several health-related behaviors in common, including
smoking (currently and ever smoked), alcohol consumption (heavy drink-
ing defined as drinking on more than 4 days per week in HRS and twice a
day or more/daily or almost daily in ELSA). While other risk factors may
be important, we used only these comparably measured variables in our
multivariate models.
Measures of Social Networks, Social Support, and Negative Interactions
Measures of the size of social networks and various forms of social
participation and quality of social support available to individuals were
measured in both surveys using almost identical questionnaires. One key
advantage of using these two surveys is that their comparable questions
cover many key domains of the social network. Questions were asked in
several domains about relationships with children, partners, close family
members, and friends. In addition, the surveys included questions about

OCR for page 217
VARIATIONS IN SOCIAL INTEGRATION AND SOCIAL INTERACTIONS
voluntary activities. With regard to children, in addition to the number of
children, respondents were asked about the frequency of their interactions
with their children on a 4-point scale (a lot, some, a little, not at all). We
coded these scores numerically from 0 to 3.
Three questions address elements of positive interaction: (1) do your
children really understand the way you feel about things, (2) can you rely on
them if you have a serious problem, and (3) can you open up to them if you
need to talk about your worries. The other three address negative interac-
tions: (1) how much do your children criticize you, (2) how much do they let
you down when you are counting on them, and (3) how much do they get
on your nerves. We separated these into two components—positive sup-
port and negative interactions—and summed the numerical scores. The
total scores for both positive support and negative support vary between 0
and 9. So that high scores on positive and negative interactions mean the
same thing, the top score of 9 for negative interactions implies no negative
interactions.
HRS and ELSA respondents were asked (not counting those children
living with you) about the frequency of contact with children on three
dimensions: (1) meeting (arranged and chance meetings), (2) speaking on the
phone, and (3) writing an email. The scale for each dimension consists of six
possible categories: (1) three or more times a week, (2) once or twice a week,
(3) once or twice a month, (4) every few months, (5) once or twice a year,
and (6) less than once a year or never. Finally, respondents were asked
with how many children they have a close relationship. Our measure does
not distinguish between individuals without children and individuals with
children who are not close or not in contact, since our measure is intended
to capture contact, which would be zero in both cases. However, to assess
whether differences between childless individuals and those with children
are influencing our results, we also estimated our models for the sample
of those with children only. The results were broadly unaffected, with one
exception: the social estimated interaction effects were slightly weaker,
suggesting that some of the identification of these effects was coming from
differences between the childless and those with children. However, since
all substantive conclusions of our analysis were unaffected (indeed, if the
interaction effects are weaker, our conclusions are strengthened) we do not
present this analysis in the tables of results.
Respondents were also asked the same set of questions about positive
and negative interactions, frequency of contact, and the number of close
relationships they have with other immediate family members, defined as
siblings, parents, cousins, or grandchildren. Friends are also a potentially
important component of any support network. HRS and ELSA ask the same
set of questions (positive and negative interactions), frequency of contact,
and number of friends. Scales for positive and negative interactions and

OCR for page 217
INTERNATIONAL DIFFERENCES IN MORTALITY AT OLDER AGES
frequency of contact are scored in the same way as for children: scores were
translated into a scale that ranges from 0 to 9. In the data in these analyses
we have summed the total of either positive or negative social interactions
across children, friends, and relatives. High scores represent high levels of
positive interaction or low levels of negative interactions (in both cases,
“high” is the more optimal interaction).
Questions about social participation in voluntary and civic organiza-
tions and religious attendance were also asked. In HRS, the item about vol-
untary activity was framed in terms of frequency of participation, whereas in
ELSA it was asked as the number of organizations the participant belonged
to. Ties with religious organizations were assessed by attendance. Finally,
we developed a summary index of social integration that summed network
domains related to children, partner, friends, and relatives and volunteer
and religious activities into a single score. This index has six dimensions:
(1) married/partnered, (2) frequency of visits with children, (3) frequency
of visits with family, (4) frequency of visits with friends, (5) participation
in voluntary organizations, and (6) religious attendance. The score could
range from 0 to 16, with 0 reflecting no tie and 3 in each domain reflect-
ing high levels of contact. Religious attendance, however, was scored 0 or
1 due to limitations in the availability of more nuanced measures in the
ELSA questionnaire (in the HRS-only analysis of mortality, we were able
to distinguish between those attending religious services regularly and those
attending periodically, and this distinction did prove to be important).
COMPARISONS BETWEEN ENGLAND AND THE UNITED STATES
There are several ways of characterizing social networks, including the
existence, number, and type of key people in the network and the nature of
interactions taking place, both positive and negative. Although we exam-
ined each social network domain individually, in this section we provide
tables or figures on summary measures related only to the social network
index, the summary measure, and positive and negative social interactions.
We describe social networks in England and the United States for spouses,
children, other immediate family members, and friends.
Distribution of Social Networks
We begin with a description of an aggregate index of social networks in
the two countries. While there are some differences in how older men and
women maintain contact with friends, family, and larger civic, religious,
and voluntary organizations, the overall distribution of social networks is
virtually identical in the two countries.
Figures 8-1A and 8-1B show the distribution of scores for our overall

OCR for page 217
VARIATIONS IN SOCIAL INTEGRATION AND SOCIAL INTERACTIONS
Network index, ELSA Compared to HRS
0.18
Males ELSA
0.16
Males HRS
0.14
Fraction of Population
0.12
0.10
0.08
0.0 6
0.04
0.02
0
0 1 2 3 4 5 6 7 8 9 10 11 12
Index Score
FIGURE 8-1A Distribution of scores of the index of social networks in England
and the United States among men.
SOURCES: Authors’ calculations from the Health and Retirement Survey (2004)
and the English Longitudinal Study of Ageing (2004) microdata.
Fig8-1A.eps
index of social networks for men and women in HRS and ELSA. For both
men and women, the largest numbers of people scored in the mid-range,
between 6 and 9, and this concentration of scores is almost identical in
England and the United States. Women tended to be slightly more isolated
than men, but even among U.S. women (the most isolated), only around 5
percent of older women scored 2 or lower on the summary index.
Some differences in the frequency of contact of specific ties are of some
note, but these differences are unlikely to be sufficiently large to explain
cross-country variations in health or life expectancy. The prevalence of
those with partners, children, other family members, and friends are listed
in Table 8-1. Overall, the percentages of those with children are almost
identical in the two countries, but there are some cohort differences. Among
men and women age 75 and over (those born before 1930), U.S. men and
women were more likely to have children than their English counterparts,
reflecting greater fertility in the United States among those cohorts. In ad-
dition, U.S. men, particularly those ages 65+, were more likely to be living
with a partner. Among more recent cohorts (those born in 1940 or later),
English men and women were more likely to have children than their U.S.
counterparts.
There are conflicting data on closeness of contact and relationship
with children in the two countries. For all birth cohorts ages 50+, English

OCR for page 217
VARIATIONS IN SOCIAL INTEGRATION AND SOCIAL INTERACTIONS
men and women with children were more likely to see them at least once a
month: 67 percent of English women said that they met with their children
at least once a month compared with 62 percent of U.S. women. Compa-
rable numbers for English men and U.S. men are 62 and 56 percent, respec-
tively. These differences may not be surprising, given the relative size of the
two countries and much lower mobility among the English compared with
Americans. However, one-third of Americans in this age range stated that
they are close to three or more of their children compared with a quarter
of the English.
Distributions and means of positive interactions with children, friends,
and relatives are shown in Figure 8-2A for women and men, and the distri-
bution of negative interactions in Figure 8-2B. There are some differences
between the two countries. U.S. men and women reported somewhat lower
levels of both positive and negative interactions with children, but there is
a clear preretirement and postretirement distinction to this pattern. Pre-
retirement positive interactions with children were worse for Americans,
presumably representing a conflict with work. But in postretirement (i.e.,
after age 65), the pattern switches, and Americans had greater levels of
positive interactions with their children. Americans tended to lag behind
the English, in that they experienced more negative interactions with chil-
dren at all these ages. With other family members, however, Americans
tended to experience both greater positive interactions and greater absence
of negative interactions than their English counterparts. Interestingly, there
were no cross-country differences in distributions of positive and negative
interactions with friends.
Relationship Between Social Networks, Positive and
Negative Interactions, and Five Health Outcomes
Previous evidence suggests that U.S. men and women have higher
prevalence of many chronic diseases than their English counterparts (Banks
et al., 2006). Table 8-2 shows means of selected health measures in ELSA
and HRS, which confirm that Americans had worse health than the English,
both using self-reports and biomarkers of disease. Our aim here is twofold:
to assess whether associations between social networks and support and
morbidity and health risks are similar between countries and to examine
whether differences in prevalence of these risk factors can account for
observed cross-country variations in health between the United States and
England.
Since in most cases distributions of social relations were very similar, our
goal was to see if risks or benefits of social relations varied more or less in
one country or the other. The weakness of cross-sectional analyses is that it
is impossible to determine which condition is shaping the other. In the case

OCR for page 217
INTERNATIONAL DIFFERENCES IN MORTALITY AT OLDER AGES
A. Positive interactions, women
6. 29
USA
Children
England 6.40
USA 5.83
Family
England 5.09
USA 6.13
Friends
England 6. 27
0 0.2 0.4 0.6 0.8 1.0
0 1-3 4- 6 7-9
B. Positive interactions, men
USA 5.63
Children
5.82
England
USA 5.19
Family
England 4.42
USA 5.13
Friends
England 5.18
0 0.2 0.4 0.6 0.8 1.0
0 1-3 4- 6 7-9
FIGURE 8-2A The distribution of positive interactions with children, family, and
Fig8-2A.eps
friends.
graduated fills
SOURCES: Authors’ calculations from the Health and Retirement Study (2004) and
the English Longitudinal Study of Ageing (2004) microdata.
of social relations and chronic morbidity, it is very likely that the relations
are bidirectional, with strong social ties and support influencing health in a
positive way and poor health itself placing stresses on social ties and making
interactions difficult. Still, acute illnesses tend to elicit greater expressions of
social support, and the provision of care for an ill or disabled family member
often requires frequent contact. These processes may create a spurious as-

OCR for page 217
VARIATIONS IN SOCIAL INTEGRATION AND SOCIAL INTERACTIONS
across countries. Japan, Switzerland, and the Netherlands reported among
the highest levels of social time, while Greece and the Czech Republic re-
ported relatively low levels. This question was not asked in U.S. and Cana-
dian samples. Time volunteered to an organization in the past month also
varied widely among countries, with the United States ranking highest for
both men and women (43 percent), followed by Ireland, the Netherlands,
and Norway. In several countries, less than 15 percent of the population
reported volunteering, among them Greece and Romania.
Associations with Life Expectancy
To illustrate a simple first-order relationship between life expectancy
and measures of social integration, Figures 8-5, 8-6, 8-7, and 8-8 show plots
of life expectancy at birth for men and women against country-level means
or percentages for four types of social connections or social participation:
religious attendance, partnership status, social time with friends and rela-
tives, and volunteered time. Table 8-9 presents a simple multivariate model
predicting country-level life expectancy that includes all social network
variables.
Countries with higher percentages of ties with regard to marriage had
higher life expectancy (Figures 8-5A and 8-5B). However, in our model that
controls for all measures of social ties and participation, this association
was statistically significant for women (p = .05) but not for men (p = .35).
Countries with high levels of social time also had higher life expectancy
(Figures 8-7A and 8-7B), but these associations were not significant in
multivariate models (the effect is positive but the p-values are around 0.2).
A higher percentage who volunteered their time was associated with higher
life expectancy (Figures 8-8A and 8-8B), and this association was significant
for men (p = .02) and of borderline significance for women (p = .06). Finally,
there is no correlation between life expectancy and religious attendance
(Figures 8-6A and 8-6B) or in the results shown in Table 8-9.4
This analysis indicates large variability across these countries both in life
expectancy and aggregate levels and distribution of social integration and
social ties and participation. While our results indicate that some measures
of social integration might be correlated with life expectancy, aggregated
Gallup data for these industrialized countries by themselves were not able
to distinguish sufficiently among alternative measures of social integration,
even without placing into these models other relevant health behaviors
on which countries differ. Even if we take these results at face value, their
4When gross domestic product was controlled for in analyses conducted by Deaton that
included a much larger number of countries in the Gallup poll, significant correlations were
reported for many analyses, especially for women.

OCR for page 217
INTERNATIONAL DIFFERENCES IN MORTALITY AT OLDER AGES
TABLE 8-9 Linear Regression Model of Country Life Expectancy on
Social Participation and Ties: Gallup and World Health Organization
Data
Coefficient S.E. p-value
Men (Intercept) 54.82
Religious services 5.39 5.61 0.348
Married or living with partner 17.18 14.81 0.259
Social time with friends/family 0.76 0.57 0.198
Volunteered time 16.80 7.01 0.026
Women (Intercept) 68.92
Religious services 0.10 2.49 0.969
Married or living with partner 13.98 6.83 0.053
Social time with friends/family 0.37 0.28 0.195
Volunteered time 9.06 4.59 0.062
NOTES: Coefficients indicate the change in life expectancy for a change from 0 to 1 in the
probability of the social contact variable.
SOURCE: Authors’ calculations from Gallup World Survey (2006-2007).
implications for explaining the U.S. health disadvantage are far from clear.
While America might rank relatively low on some measures of social inte-
gration, such as marriage and social ties, it ranks relatively high on other
measures, such as religious attendance and especially volunteering. Finally,
the extent to which these associations are causal, produced by reverse causa-
tion, or are the result of underlying variations in third factors, such as gross
domestic product, needs to be adequately examined in future research. Our
purpose is to illustrate the window of opportunity to examine these issues
by capitalizing on variations across countries in social integration and life
expectancy.
IMPLICATIONS
In this chapter we attempt to assess whether aspects of social relation-
ships and social participation might account for country differences in mor-
bidity and life expectancy. The findings from our cross-sectional analyses
and 3- to 5-year follow-ups suggest that current differences in these social
conditions between the United States and England do not explain current
differences in mortality or morbidity. First, observed differences in social
networks and support between these two countries are small. Second, we
found weak and inconsistent effects of the social network and support vari-
ables on the health outcomes we considered, with few associations reaching
conventional levels of statistical significance.

OCR for page 217
VARIATIONS IN SOCIAL INTEGRATION AND SOCIAL INTERACTIONS
Our analyses highlight the difficulty in undertaking comparative analy-
ses in these domains. Even with tightly harmonized studies, such as HRS and
ELSA, some differences in measurement remain, and mortality follow-up
periods for studies with relevant social network constructs remain relatively
short—5 years for ELSA and 3 years for HRS. Only time will tell whether
these factors affect our results, as future data waves become available.
The potential contribution of future data and analysis derived from long-
term follow-ups of these and other, even more tightly harmonized cohorts
is clear.
We found remarkable similarities in the cross-sectional distributions of
social contacts and participation between the United States and England. We
focused on these two countries because comparable data on social contacts
and health were available for these populations, and recent research has
demonstrated that health differences in morbidity are large. However, given
the similarity in the two distributions, this focus also limited our ability to
detect the potential role that these factors might have in a context of wider
variation in social contacts and support. Our descriptive analysis based on
the Gallup survey illustrates this limitation by pointing out the much larger
variability in social contacts in other industrialized nations. Our analysis
of England and the United States might not reveal the full potential contri-
bution of social networks and social support to health differences across
a broader set of countries. In exploring whether social networks might
account for cross-country differences, priority should therefore be given to
harmonizing data across countries that allow us to test this hypothesis in a
broader international context.
A second issue refers to what the appropriate measures of social net-
works and participation might be. We have focused here on self-reports of
frequency of contacts and levels of positive and negative support in England
and America. Beyond these measures, there may be other key aspects, in-
cluding how close relationships truly are and whether individuals feel they
can rely on a social network. These less tangible aspects of social networks
might have health effects not captured by the measures in our surveys. For
example, some studies suggest that it might be the perception of social
connectedness rather than the actual level of social support that influences
health outcomes (Ashida and Heaney, 2008). Others have argued that one
special friend or relative is the key concept, implying that the nature of the
relations with others may not be relevant. Compared with many areas of
determinants of health, the development of conceptual measures of social
networks and support is relatively recent. It is fair to say that the field has
not yet reached a consensus on the most appropriate set of conceptual
measures, especially harmonized measures in an international comparative
context.
Besides social networks and integration, other aspects of social behavior

OCR for page 217
INTERNATIONAL DIFFERENCES IN MORTALITY AT OLDER AGES
not incorporated into our study could be important in explaining health dif-
ferences among countries. In addition, social contacts may influence health
via several distinct mechanisms, including social regulation and behavioral
norms; direct contagion of disease; transfer of material resources or infor-
mation; positive emotional experiences, such as feeling loved, valued, or
“belonging” to a group; or negative emotional experiences, such as shame
or loneliness (Berkman and Glass, 2000). The importance of each pathway
may depend on the specific health outcome—for example, smoking behavior
may be very responsive to norms and social regulation, whereas they may
be less relevant for breast cancer survival rates. Our study focuses primarily
on whether networks and support have an overall association with health
outcomes, but future studies should examine whether other social mecha-
nisms might contribute to health differences across countries.
A fourth issue refers to differences in reporting styles among countries.
While we found no differences in levels of social support and networks
between English and U.S. respondents, many measures rely on subjective
scales that have been shown in other contexts to exhibit considerable in-
ternational variation (Kapteyn, Smith, and VanSoest, 2007). Individuals in
each country might report their level of contact using different reporting
thresholds, which may in turn influence their answers to these subjective
questions. Additional investigations, perhaps including the use of vignettes,
are needed in order to evaluate heterogeneity in reporting styles and, if such
heterogeneity exists, to identify true differences in the distribution of social
networks and support among countries.
A final set of issues relates to the fact that our analysis has been pre-
dominantly cross-sectional in nature, out of necessity given the availability
of comparable data. As such, we can neither investigate nor control for
intertemporal or, for that matter, intergenerational issues. This has a number
of consequences. First, we can say nothing about how current differences
across countries (to the extent they exist) in social integration and inter-
actions might affect future life expectancy, nor how past trends in social
integration are related to past trends in life expectancy. Second and closely
related, to the extent that there are differences among countries in the level
and trajectories of past social interactions and this history matters for cur-
rent health and mortality outcomes, these differences are uncontrolled for in
our study. Once again, when one extends the set of countries being analyzed
beyond the United States and England, this may be an even more important
issue than when considering these two countries alone. For example, to the
extent that historical trajectories in Europe and the former Soviet Union
countries differ for marriage, age of childbearing, and single parenthood,
there may well be knock-on effects onto past trajectories of social support
and integration, which could plausibly affect life-course health and mortal-
ity outcomes, and hence life expectancy, in these countries. Given the data

OCR for page 217
VARIATIONS IN SOCIAL INTEGRATION AND SOCIAL INTERACTIONS
available, investigation of such a hypothesis is beyond the scope or capacity
of our analysis. Similarly, it is also impossible to investigate the hypothesis
that one possible role of social integration and support is alleviating or miti-
gating the consequences of adverse shocks when they happen, given the lack
of internationally comparable historical data. The intuitive plausibility of
such intertemporal hypotheses suggests that data collection activities should
be prioritized in order to facilitate analyses of these issues in the future.
Taken together, the analyses of this chapter and the caveats in the
discussion above suggest that future research should focus on identifying
multiple measures that can capture the most relevant aspects of the life-
course trajectories of social networks, integration, and support that might
be important to health, as well as developing strategies to make these
measures comparable across countries. Until that happens, claims about
the power of social network constructs to explain international health dif-
ferences are still premature. Such an approach might also yield a new line
of research that will allow the testing of the role of social networks and
support in explaining diverging trends in life expectancy in a wider set of
industrialized nations.
REFERENCES
Ashida, S., and Heaney, C.A. (2008). Differential associations of social support and social
connectedness with structural features of social networks and the health status of older
adults. Journal of Aging Health, 0(7), 872-893.
Banks, J., Marmot, M., McMunn, A., and Smith, J.P. (n.d.). The English Are Healthier Than
Americans: Do Social Risk-Factors Contribute? Manuscript in preparation.
Banks, J., Marmot, M., Oldfield, Z., and Smith, J.P. (2006). Disease and disadvantage in the
United States and in England. Journal of the American Medical Association, (17),
2037-2045.
Berkman, L.F., and Glass, T. (2000). Social integration, social networks, social support, and
health. In L.F. Berkman and I. Kawachi (Eds.), Social Epidemiology (pp. 137-173). New
York: Oxford University Press.
Berkman, L.F., and Syme, S.L. (1979). Social networks, host resistance, and mortality: A nine-
year follow-up study of Alameda County residents. American Journal of Epidemiology,
0(2), 186-204.
Berkman, L.F., Melchior, M., Chastang, J.F., Niedhammer, I., Leclerc, A., and Goldberg, M.
(2004). Social integration and mortality: A prospective study of French employees of
Electricity of France-Gas of France: The GAZEL Cohort. American Journal of Epidemi-
ology, (2), 167-174.
Blazer, D.G. (1982). Social support and mortality in an elderly community population. Ameri-
can Journal of Epidemiology, (5), 684-694.
Deaton, A. (2009). Aging, Religion, and Health. NBER Working Paper w15271. Cambridge,
MA: National Bureau of Economic Research.
Fuhrer, R., and Stansfeld, S.A. (2002). How gender affects patterns of social relations and
their impact on health: A comparison of one or multiple sources of support from “close
persons.” Social Science & Medicine, (5), 811-825.

Bookmark this page

Important Notice

As of 2013, the National Science Education Standards have been replaced by the Next Generation Science Standards (NGSS), available as a print book, free PDF download, and online with our OpenBook platform.